Maps for each races

library(ggplot2)
library(plotly)

## 
## Attaching package: 'plotly'

## The following object is masked from 'package:ggplot2':
## 
##     last_plot

## The following object is masked from 'package:stats':
## 
##     filter

## The following object is masked from 'package:graphics':
## 
##     layout

library(htmlwidgets)
library(patchwork)

data <- read.csv("combined_all_means_with_state.csv")
data$state = data$State

race_data <- data %>%
  select(starts_with("Bene_Race"), starts_with("State")) %>%
  select(where(~!any(is.na(.))))

race_columns <- grep("Bene_Race", names(race_data), value = TRUE)

race_data_cnt <- race_data %>%
  mutate(Total_Benes = rowSums(select(., all_of(race_columns))))

race_data_pct <- race_data_cnt %>%
  mutate(across(all_of(race_columns), ~ .x / Total_Benes * 100, .names = "{.col}_Pct")) %>%
  select(ends_with("Pct"), ends_with("State"))

race_data_pct$state = race_data_pct$State

us_map <- map_data("state")
state_lookup <- data.frame(state = c("AL", "AK", "AZ", "AR", "CA", "CO", "CT", "DE", "FL", "GA", "HI", "ID", "IL", "IN", "IA", "KS", "KY", "LA", "ME", "MD", "MA", "MI", "MN", "MS", "MO", "MT", "NE", "NV", "NH", "NJ", "NM", "NY", "NC", "ND", "OH", "OK", "OR", "PA", "RI", "SC", "SD", "TN", "TX", "UT", "VT", "VA", "WA", "WV", "WI", "WY"),
                           state_name = c("alabama", "alaska", "arizona", "arkansas", "california", "colorado", "connecticut", "delaware", "florida", "georgia", "hawaii", "idaho", "illinois", "indiana", "iowa", "kansas", "kentucky", "louisiana", "maine", "maryland", "massachusetts", "michigan", "minnesota", "mississippi", "missouri", "montana", "nebraska", "nevada", "new hampshire", "new jersey", "new mexico", "new york", "north carolina", "north dakota", "ohio", "oklahoma", "oregon", "pennsylvania", "rhode island", "south carolina", "south dakota", "tennessee", "texas", "utah", "vermont", "virginia", "washington", "west virginia", "wisconsin", "wyoming"))
aggregate_df1 <- merge(race_data_pct, state_lookup, by="state", all.x=TRUE)
aggregate_df1 <- aggregate_df1[!is.na(aggregate_df1$state_name), ]
merged_data1 <- merge(us_map, aggregate_df1, by.x="region", by.y="state_name", all.x=TRUE)

race_pct_columns <- grep("_Pct$", names(merged_data1), value = TRUE)

cc_data <- data %>%
  select(starts_with("Bene_CC"), starts_with("state")) %>%
  select(where(~!any(is.na(.))))

cc_columns <- grep("Bene_CC", names(cc_data), value = TRUE)

aggregate_df2 <- merge(cc_data, state_lookup, by="state", all.x=TRUE)
aggregate_df2 <- aggregate_df2[!is.na(aggregate_df2$state_name), ]
merged_data2 <- merge(us_map, aggregate_df2, by.x="region", by.y="state_name", all.x=TRUE)

pAF <- ggplot(merged_data2, aes(x=long, y=lat, group=group,
                               fill=Bene_CC_AF_Pct)) +
    geom_polygon(color="black") +
    scale_fill_gradient(low="lightblue1", high="deepskyblue3", na.value="grey") +
    labs(fill="Bene_CC_AF_Pct") +  
    coord_map() +
    theme_void() +
    ggtitle("Map of Beneficiaries Identified With Atrial Fibrillation Percentage") 

pAF <- pAF + theme(legend.text = element_text(size = 3),
                 legend.title = element_text(size = 3),
                 legend.position = "bottom",
                 legend.key.size = unit(0.3, "cm"),
                 legend.spacing = unit(0.1, "cm"))
pAF <- ggplotly(pAF)

library(htmltools)
htmltools::div(style = "display: flex;",
  htmltools::div(style = "width: 50%;", pW),
  htmltools::div(style = "width: 50%;", pAF)
)

htmltools::div(style = "display: flex;",
  htmltools::div(style = "width: 50%;", pH),
  htmltools::div(style = "width: 50%;", pDb)
)

htmltools::div(style = "display: flex;",
  htmltools::div(style = "width: 50%;", pN),
  htmltools::div(style = "width: 50%;", pAs)
)

htmltools::div(style = "display: flex;",
  htmltools::div(style = "width: 50%;", pA),
  htmltools::div(style = "width: 50%;", pCK)
)